Applications that use and display compressed video often must prepare video for transmission over a communications pipeline such as ordinary phone lines (i.e., Plain Old Telephone Service) (POTS)) or Integrated Services Digital Network (ISDN), which provide a limited bandwidth through which the video or image must be transmitted. In particular, an image is defined to include both a still image or a video frame within a stream of video frames. This limitation on the bandwidth imposes a qualitative upper bound on the video compressed with state of the art algorithms. In general, the compressing of video or image data is a lossy process and a consequence thereof is the manifestation of compression artifacts which are products introduced into the image as a result of compression that do not exist otherwise. Moreover, compression subjects the image to a general loss of detail. Despite these limitations, application users, typically, want bigger images (i.e., images occupying a significantly greater percentage of the display screen area) to see scene detail.
When images are small, the eye is forgiving. However when scaling images to larger sizes, the small defects (e.g., the artifacts or loss of detail and texture) due to the compression become extremely visible causing the image to frequently appear "washed out" as the same information is spread over a larger area. To avoid this "washed out" effect, sharpening filters are applied to help bring some more definition and crispness to images. However, compression artifacts and other defects must be taken into account during the sharpening process. Otherwise, these defects are sharpened along with the image, causing the image to look even worse than if no sharpening was performed. Therefore, for these and other reasons there is a need to adaptively sharpen certain portions of an image.